The PPR-ME algorithm that combines both ideas is simple, computationally efficient, yet significantly more sample-efficient not only than the recently-proposed \(\mathcal {A}_{1}\)  algorithm [1]}, but many other baselines (including an improved variant of \(\mathcal {A}_{1}\)  that we include in our experiments). To illustrate the practical relevance of PPR-ME, we present two separate real-world applications in which it can offer significant advantages over earlier approaches. In sample-based election forecasting, PPR-ME significantly reduces the sample complexity when paired with the DCB sampling strategy. Analysing the sample complexity of DCB in terms of its problem parameters remains an avenue for future work, which could contribute to a growing literature on sample-based techniques in social choice theory [2]}, [3]}. Our second application, to probabilistic verification in blockchains, is based on PPR-ME being relatively assumption-free, which makes it a promising algorithm for many industrial applications.
